Australian diagnostic pathology laboratories operate under strict regimes of quality monitoring testing, with programs run by both the National Association of Testing Authorities (NATA) and the Quality Assurance Program of the Royal College of Pathologists of Australasia (RCPA-QAP). In this talk I will report on a novel data linkage project that combined data from the two sources from 21 anonymous pathology laboratories in NSW, Australia. Both classical and machine learning methods were investigated, with the aim of developing predictive models to improve pathology laboratory quality by rapidly identifying declining quality. Random forest and multiple regression models proved to be the most fruitful, identifying several diagnostic markers that could provide a means of systematically monitoring laboratory performance in between the ISO15189 assessments.

Dr Alice Richardson

National Centre for Epidemiology & Population Health

Australian National University, Canberra

Alice Richardson studied at Victoria University of Wellington, New Zealand, then at the Australian National University, Canberra. Her PhD was on the statistical properties of robust methods of estimation for multilevel linear models.

She has twenty years of experience in teaching undergraduate statistics at the University of Canberra. During that time she also collaborated on quantitative research projects in every Faculty of the University, including projects in Linguistics, Human Resource Management and Biomedical Science. In 2016 Alice took up a position as biostatistician at the National Centre for Epidemiology & Population Health at the Australian National University. Here she collaborates on projects involving analysis of randomised trials as well as predictive modelling of administrative data. She is also supervising a PhD student working on multiple imputation for missing data in multilevel models.

Alice’s research interests are in linear models and robust statistics; statistical properties of data mining methods; applications of statistical methods to large data sets especially in population health and the biomedical sciences; and innovation in statistics education.